Beta rhythms (15-29 Hz) are one of the most dominant brain signals measured non-invasively in humans with magento- and electro-encephalography (MEG/EEG). They are strong predictors of perception and motor performance, and disrupted in disease states, such as Parkinson?s Disease. Yet, beta?s causal role in function is still unknown. In this proposal, we will combine human EEG, transcranial magnetic stimulation (TMS) and biophysically principled neural modeling to investigate a direct causal relationship between beta and perception and to define novel TMS paradigms that optimally impact perception. Our prior studies have shown that prestimulus beta activity measured with MEG in human primary somatosensory cortex (SI) ?inhibits? tactile detection, such that the higher the averaged prestimulus beta power the less likely the subject detects a threshold level tap to the finger. Further, averaged beta power increases in non-attended regions, presumably as a means to filter distracting information to facilitate perception (Jones et al J. Neurosci. 2010). More recently, we reported that that high power beta activity emerges as brief ?events? (<150ms) in unaveraged data, with a dominant peak lasting one beta period ~40-60ms (Sherman et al PNAS 2016). Preliminary data suggests such beta events are intermittent and that the rate of beta events underlies beta?s shift with attention and impact on perception. Prestimulus beta event rates decrease in attended, and increase in non-attended somatotopic regions (areas of distraction), corresponding with a higher probability of detection. We predict detection of tactile stimulation in an attended location will be inhibited when tactile stimulation is delivered after high spontaneous EEG beta event rates in the corresponding somatotopic region, and enhanced when delivered after high beta event rates in the non-attended somatotopic region (area of distraction) (Aim 1). We further predict TMS protocols that mimic endogenous beta event patterns will impact perception more effectively than non-TMS conditions and standard functionally ?inhibitory? TMS protocols (Aim 2). Computational neural modeling specifically designed by our group to simulate macro-scale EEG signals will be used to intepret circuit mechanisms underlying observed data (Aim 3). !